{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06153901422618192"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3426/(3426+52246)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06121568538407393"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2696/(2696+41345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages=[-1.,  0.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13., 14.,15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40.,41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53.,54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66.,67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98.]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(missidentification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "missidentification=[2, 51, 2, 7, 13, 8, 3, 10, 16, 13, 11, 3, 6,8, 15, 12, 11, 17, 8, 22, 14, 24, 25, 20, 35, 37,42, 34, 39, 48, 35, 33, 50, 33, 40, 27, 39, 50, 60,65, 58, 75, 65, 85, 103, 114, 129, 110, 139, 132, 152, 167,161, 129, 102, 114, 112, 112, 137, 171, 146, 207, 153, 157, 134,153, 134, 154, 122, 153, 134, 92, 82, 75, 76, 78, 82, 64,80, 74, 85, 80, 62, 42, 44, 24, 17, 14, 11, 16, 10,5, 1, 4, 2, 0,2,0,0,0]\n",
    "identifications=[31, 605, 41, 125, 138, 163, 107, 155, 138, 146, 91,77, 93, 122, 220, 208, 164, 229, 256, 342, 332, 408,451, 299, 556, 527, 602, 606, 734, 603, 485, 490, 658,556, 687, 514, 661, 702, 804, 901, 774, 1074, 1243, 1169,1437, 1737, 2015, 1678, 1785, 2121, 2206, 2361, 2267, 2128, 1670,1594, 1967, 1763, 2386, 2545, 2270, 2876, 2163, 2514, 2048, 2507,2297, 2489, 2210, 2257, 1959, 1267, 1318, 1366, 1196, 1071, 1120,1049, 1189, 1104, 1270, 1211, 812, 632, 492, 379, 300, 215,266, 273, 222, 80, 79, 81, 41, 8, 18,0,0,0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "identifications = np.array(identifications)\n",
    "missidentification = np.array(missidentification)\n",
    "total = identifications+missidentification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "misiden_ratio=np.divide(missidentification,total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06060606, 0.0777439 , 0.04651163, 0.0530303 , 0.08609272,\n",
       "       0.04678363, 0.02727273, 0.06060606, 0.1038961 , 0.08176101,\n",
       "       0.10784314, 0.0375    , 0.06060606, 0.06153846, 0.06382979,\n",
       "       0.05454545, 0.06285714, 0.06910569, 0.03030303, 0.06043956,\n",
       "       0.04046243, 0.05555556, 0.05252101, 0.06269592, 0.05922166,\n",
       "       0.06560284, 0.06521739, 0.053125  , 0.05045278, 0.07373272,\n",
       "       0.06730769, 0.06309751, 0.07062147, 0.05602716, 0.05502063,\n",
       "       0.04990758, 0.05571429, 0.06648936, 0.06944444, 0.06728778,\n",
       "       0.06971154, 0.06527415, 0.04969419, 0.06778309, 0.06688312,\n",
       "       0.06158833, 0.06016791, 0.06152125, 0.07224532, 0.05858855,\n",
       "       0.06446141, 0.06606013, 0.06630972, 0.05715552, 0.05756208,\n",
       "       0.06674473, 0.05387205, 0.05973333, 0.05430044, 0.06296024,\n",
       "       0.06043046, 0.06714239, 0.06606218, 0.05877948, 0.06141155,\n",
       "       0.0575188 , 0.05512135, 0.05826712, 0.05231561, 0.06348548,\n",
       "       0.06402293, 0.06769684, 0.05857143, 0.05204719, 0.05974843,\n",
       "       0.06788512, 0.06821963, 0.05750225, 0.06304177, 0.06281834,\n",
       "       0.06273063, 0.06196747, 0.07093822, 0.06231454, 0.08208955,\n",
       "       0.05955335, 0.05362776, 0.06113537, 0.03971119, 0.05536332,\n",
       "       0.04310345, 0.05882353, 0.0125    , 0.04705882, 0.04651163,\n",
       "       0.        , 0.1       ,        nan,        nan,        nan])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misiden_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113045c50>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " plt.plot( misiden_ratio) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "identifications_grouped = identifications.reshape(10,10)\n",
    "missidentification_grouped = missidentification.reshape(10,10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "identifications_grouped_sum=np.sum(identifications_grouped, axis=1)\n",
    "missidentification_grouped_sum=np.sum(missidentification_grouped, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_grouped = identifications_grouped_sum+missidentification_grouped_sum\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "misiden_grouped_ratio=np.divide(missidentification_grouped_sum,total_grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "x = ['0-10', '10-20', '20-30', '30-40', '40-50', '40-50', '50-60', '60-70', '80-90', '90-100']\n",
    "ax.bar(x,misiden_grouped_ratio)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1774,  1915,  5436,  6890, 16043, 22244, 25144, 13476,  6245,\n",
       "         553])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07046223, 0.05900783, 0.0584989 , 0.06269956, 0.06295581,\n",
       "       0.06100521, 0.0601734 , 0.06211042, 0.0632506 , 0.04339964])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misiden_grouped_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "identifications_grouped = identifications.reshape(2,50)\n",
    "missidentification_grouped = missidentification.reshape(2,50)\n",
    "identifications_grouped_sum=np.sum(identifications_grouped, axis=1)\n",
    "missidentification_grouped_sum=np.sum(missidentification_grouped, axis=1)\n",
    "total_grouped = identifications_grouped_sum+missidentification_grouped_sum\n",
    "misiden_grouped_ratio=np.divide(missidentification_grouped_sum,total_grouped)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06232454, 0.06097957])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misiden_grouped_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
